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A Spatial Autoregressive Combination Stochastic Frontier Model Separating Spatial Dependence in the Frontier and in Technical Inefficiency
Tsukamoto, Takahiro - 2019
We identify the attributes and limitations of spatial autoregressive stochastic frontier models, spatial inefficiency …) spatial dependence in the frontier and in technical inefficiency are separately identified; (b) a negative spatial … autocorrelation in technical inefficiency is permitted; (c) technical inefficiency follows a truncated-normal distribution …
Persistent link: https://www.econbiz.de/10012896151
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A Spatial Autoregressive Stochastic Frontier Model for Panel Data Incorporating a Model of Technical Inefficiency
Tsukamoto, Takahiro - 2019
variables and the joint structure of a production possibility frontier with a model of technical inefficiency. The model … addresses both spatial dependence and heteroskedastic technical inefficiency. This study applies maximum likelihood methods …-spatial models may exhibit bias because of lack of determinants of technical inefficiency, as well as a spatial lag. This bias also …
Persistent link: https://www.econbiz.de/10012896405
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A Spatial Autoregressive Production Frontier Model for Panel Data : With an Application to European Countries
Glass, Anthony; Glass, Karligash; Sickles, Robin C. - 2013
In this paper we merge techniques from the efficiency literature with spatial econometric techniques. In particular, we combine calculation of efficiency from the unit speci c effects with the spatial lag model to develop a spatial autoregressive frontier model for panel data. Features of the...
Persistent link: https://www.econbiz.de/10014160248
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A Spatial Autoregressive Stochastic Frontier Model for Panel Data Incorporating a Model of Technical Inefficiency
Tsukamoto, Takahiro - 2018
variables and the joint structure of a production possibility frontier with a model of technical inefficiency. The model … addresses both spatial dependence and heteroskedastic technical inefficiency. This study applies maximum likelihood methods …-spatial models may exhibit bias because of lack of determinants of technical inefficiency, as well as a spatial lag. This bias also …
Persistent link: https://www.econbiz.de/10014116814
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Productivity Growth Decomposition Using a Spatial Autoregressive Frontier Model
Glass, Anthony - 2013
A new spatial decomposition of TFP growth into direct (own) and indirect (spillover) components is set out. We then apply the decomposition in the context of a spatial autoregressive production frontier analysis of 40 European countries over the period 1995-2008
Persistent link: https://www.econbiz.de/10013086032
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Gaussian Analysis of Non-Gaussian Time Series
Kugiuntzis, Dimitris; Bora-Senta, Efthimia - In: Brussels Economic Review 53 (2010) 2, pp. 295-322
A framework is proposed for the analysis of non-Gaussian time series under the Gaussian assumption. The analysis is based on the Gaussian autocorrelation computed from the transform of the sample autocorrelation. It is shown that this approach improves the linear autoregressive fit. We also use...
Persistent link: https://www.econbiz.de/10008873419
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Model averaging by jackknife criterion in models with dependent data
Zhang, Xinyu; Wan, Alan T.K.; Zou, Guohua - In: Journal of Econometrics 174 (2013) 2, pp. 82-94
The past decade witnessed a literature on model averaging by frequentist methods. For the most part, the asymptotic optimality of various existing frequentist model averaging estimators has been established under i.i.d. errors. Recently, Hansen and Racine [Hansen, B.E., Racine, J., 2012....
Persistent link: https://www.econbiz.de/10010664706
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Statistical Methodological Issues in Studies of Air Pollution and Respiratory Disease.
Hyndman, R.J.; Erbas, B. - Department of Econometrics and Business Statistics, … - 2001
Epidemiological studies have consistently shown short term associations between levels of air pollution and respiratory disease in countries of diverse populations, geographical locations and varying levels of air pollution and climate. The aims of this paper are: (1) to assess the sensitivity...
Persistent link: https://www.econbiz.de/10005149105
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Generalized autoregressive method of moments
Creal, Drew; Koopman, Siem Jan; Lucas, André; … - 2015
We introduce a new estimation framework which extends the Generalized Method of Moments (GMM) to settings where a subset of the parameters vary over time with unknown dynamics. To filter out the dynamic path of the time-varying parameter, we approximate the dynamics by an autoregressive process...
Persistent link: https://www.econbiz.de/10011431471
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Evaluating eigenvector spatial filter corrections for omitted georeferenced variables
Griffith, Daniel A.; Chun, Yongwan - In: Econometrics : open access journal 4 (2016) 2, pp. 1-12
The Ramsey regression equation specification error test (RESET) furnishes a diagnostic for omitted variables in a linear regression model specification (i.e., the null hypothesis is no omitted variables). Integer powers of fitted values from a regression analysis are introduced as additional...
Persistent link: https://www.econbiz.de/10011506413